Video-based human recognition at a distance remains a challenging problem for the fusion of multimodal
biometrics. We present a
new approach that utilizes and integrates information from side face and gait at the feature level. The features of
face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image
(ESFI) and gait energy image (GEI), respectively. Multiple discriminant analysis (MDA) is employed on the
concatenated features of face and gait to obtain discriminating synthetic features. The experimental results demonstrate that the synthetic features, encoding both side face and gait
information, carry more discriminating power than the individual biometrics features, and the proposed feature
level fusion scheme outperforms the match score level and another feature level fusion scheme.

We present a linear genetic programming approach,
that solves simultaneously the region selection and feature extraction
tasks, that are applicable to common image recognition problems. The
method searches for optimal regions of interest, using texture information
as its feature space and classification accuracy as the fitness function.
Texture is analyzed based on the gray level cooccurrence matrix
and classification is carried out with a SVM committee. Results show
effective performance compared with previous results using a standard
image database.

Among the existing feature selection/synthesis approaches, Coevolutionary Feature Synthesis (CFS) based on Coevolutionary Genetic Programming (CGP) has shown good performance on a variety of applications. In this paper, we propose an MDL-based fitness function to help pick a reasonable number of synthesized features which is equal to the number of subpopulations. It naturally balances the feature transformation complexity and classification performance. Experiments on a real image database show that the new fitness function solves the problem quite well.

As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. The evolutionary computation has also attracted a lot of attention in pattern recognition and has shown significant performance improvement on a variety of applications. However, there has been no comparison of the two methods. In this paper, first we propose an improvement of a coevolutionary computational classification algorithm, called Improved Coevolutionary Feature Synthesized EM (I-CFS-EM) algorithm. It is a hybrid of coevolutionary genetic programming and EM algorithm applied on partially labeled data. It requires less labeled data and it makes the test in a lower dimension, which speeds up the testing. Then, we provide a comprehensive comparison between SVM with different kernel functions and I-CFS-EM on several real datasets. This comparison shows that I-CFS-EM outperforms SVM in the sense of both the classification performance and the computational efficiency in the testing phase. We also give an intensive analysis of the pros and cons of both approaches.

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.

We propose a novel genetically
inspired learning method for facial expression recognition (FER). Our learning method can select visually meaningful
features automatically in a genetic programming-based approach that uses Gabor wavelet representation for
primitive features and linear/nonlinear operators to synthesize new features. To make use of random nature of a genetic
program, we design a multi-agent scheme to boost the performance. We compare the performance of our
approach with several approaches in the literature and show that our approach can perform the task of facial
expression recognition effectively.

In this paper, we propose a unified framework of a novel learning approach, namely Coevolutionary Feature Synthesized Expectation-Maximization (CFSEM), to achieve satisfactory learning in spite of these difficulties. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm. The advantages of CFS-EM are: 1) it synthesizes low-dimensional features based on CGP algorithm, which yields near optimal nonlinear transformation and classification precision comparable to kernel methods such as the support vector machine (SVM); 2) the explicitness of feature transformation is especially suitable for image retrieval because the images can be searched in the synthesized low-dimensional space, while kernel-based methods have to make classification computation in the original high-dimensional space; 3) the unlabeled data can be boosted with the help of the class distribution learning using CGP feature synthesis approach. Experimental results show that CFS-EM outperforms pure EM and CGP alone, and is comparable to SVM in the sense of classification. It is computationally more efficient than SVM in query phase. Moreover, it has a high likelihood that it will jump out of a local maximum to provide near optimal results and a better estimation of parameters.

We propose a coevolutionary genetic
programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to
overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very
large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. The comparison with other classical classification
algorithms is favourable to the CGP-based approach proposed in our research.

In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.

We use genetic programming (GP) to synthesize composite operators and composite features
from combinations of primitive operations and primitive features for object detection. The motivation for
using GP is to overcome the human experts' limitations of focusing only on conventional combinations of
primitive image processing operations in the feature synthesis. GP attempts many unconventional
combinations that in some cases yield exceptionally good results. Our experiments, which are performed on selected
training regions of a training image to reduce the training time, show that compared to normal GP, our GP
algorithm finds effective composite operators more quickly and the learned composite operators can be
applied to the whole training image and other similar testing images.

Genetic programming (GP) is applied to synthesize composite operators from primitive
operators and primitive features for object detection. To improve the efficiency of GP, smart crossover,
smart mutation and a public library are proposed to identify and keep the effective components of
composite operators. To prevent code bloat and avoid severe restriction on the GP search, a MDL-based
fitness function is designed to incorporate the size of composite operator into the fitness evaluation process.
The experiments with real synthetic aperture radar (SAR) images show that compared to normal GP, GP
algorithm proposed here finds effective composite operators more quickly.

We present a fingerprint classification approach based on a novel feature-learning algorithm.
Unlike current research for fingerprint classification that generally uses well defined meaningful features,
our approach is based on Genetic Programming (GP), which learns to discover composite operators and
features that are evolved from combinations of primitive image processing operations. Our experimental
results show that our approach can find good composite operators to effectively extract useful features.

In this paper, we investigate the effectiveness of coevolutionary genetic programming (CGP) in synthesizing feature vectors for image databases from traditional features that are commonly used. The transformation for feature dimensionality reduction by CGP has two unique characteristics for image retrieval: 1) nonlinearlity: CGP does not assume any class distribution in the original visual feature space; 2) explicitness: unlike kernel trick, CGP yields explicit transformation for dimensionality reduction so that the images can be searched in the low-dimensional feature space. The experimental results on multiple databases show that (a) CGP approach has distinct advantage over the linear transformation approach of Multiple Discriminant Analysis (MDA) in the sense of the discrimination ability of the low-dimensional features, and (b) the classification performance using the features synthesized by our CGP approach is comparable to or even superior to that of support vector machine (SVM) approach using the original visual features.

In this paper, we learn to discover composite operators and features that are synthesized from combinations of primitive image processing operations for object detection. Our approach is based on genetic programming (GP). The motivation for using GP-based learning is that we hope to automate the design of object detection system by automatically synthesizing object detection procedures from primitive operations and primitive features. The human expert, limited by experience, knowledge and time, can only try a very small number of conventional combinations. Genetic programming, on the other hand, attempts many unconventional combinations that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results.

In this paper we introduce a novel sensor fusion algorithm based on the cooperative coevolutionary paradigm. We develop a multisensor robust moving object detection system that can operate under a variety of illumination and environmental conditions. Our experiments indicate that this evolutionary paradigm is well suited as a sensor fusion model and can be extended to different sensing modalities.

Genetic programming (GP) with smart crossover and smart mutation is proposed in our research to
discover integrated feature agents that are evolved from combinations of primitive image processing
operations to extract regions-of-interest (ROIs) in remotely sensed images. Smart
crossover and smart mutation identify and keep the effective components of integrated operators called
"agents" and significantly improve the efficiency of GP. Our experimental results show that compared to
normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly
during training to effectively extract the regions-of-interest and the learned agents can be applied to extract
ROIs in other similar images.

Advances in the area of autonomous mobile robotics have allowed robots to explore vast and often unknown terrains. This paper presents a particular form of autonomy that allows a robot to autonomously control its speed, based on perception, while traveling on unknown terrain. The robot is equipped with an onboard camera and a 3-axis accelerometer. The method begins by classifying a query image of the terrain immediately before the robot. Classification is based on the Gabor wavelet features. In learning the speed, a genetic algorithm is used to map the Gabor texture features to approximate speed that minimizes changes in accelerations along the three axes from their nominal values. Learning is performed continuously. Experiments are done in real time.

We present an approach to automatic image segmentation, in which user selected sets of examples and
counter-examples supply information about the specific segmentation problem. In our approach, image
segmentation is guided by a genetic algorithm which learns the appropriate subset and spatial combination
of a collection of discriminating functions, associated with image features. The genetic algorithm encodes
discriminating functions into a functional template representation, which can be applied to the input image
to produce a candidate segmentation.

Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key factors to the performance of object recognition. In this paper, we propose a co-evolutionary genetic programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. On the other hand, CGP can try a very large number of unconventional combinations and these unconventional combinations may yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can learn good composite features. We show results to distinguish objects from clutter and to distinguish objects that belong to several classes.

This paper introduces a novel visual learning method that involves cooperative coevolution and linear genetic programming. Given exclusively training images, the evolutionary learning algorithm induces a set of sophisticated feature extraction agents represented in a procedural way. The proposed method incorporates only general vision-related background knowledge and does not require any task-specific information. The paper describes the learning algorithm, provides a firm rationale for its design, and proves its competitiveness with the human-designed recognition systems in an extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery.

We have learned through this research to discover composite operators and features that are evolved from combinations
of primitive image processing operations to extract regions-of-interest (ROIs) in images. Our approach is
based on genetic programming (GP). The motivation for using GP is that there are a great many ways of
combining these primitive operations and the human expert, limited by experience, knowledge and time,
can only try a very small number of conventional ways of combination. Genetic programming, on the other
hand, attempts many unconventional ways of combination that may never be imagined by human experts.
In some cases, these unconventional combinations yield exceptionally good results. Our experimental
results show that GP can find good composite operators, that consist of primitive operators designed
by us, to effectively extract the regions of interest in images and the learned composite operators
can be applied to extract ROIs in other similar images.

A genetic algorithm (GA) approach is presented to select a set of features to discriminate the targets from
the natural clutter false alarms in SAR images. A new fitness function based on minimum
description length principle (MDLP) is proposed to drive GA and it is compared with three other fitness
functions. Experimental results show that the new fitness function outperforms the other three fitness
functions and the GA driven by it selected a good subset of features to discriminate the targets from
clutters effectively.

In this paper, we apply genetic programming (GP) with smart crossover and smart mutation to discover integrated feature agents that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and smart mutation identify and keep the effective components of integrated operators called "agents" and significantly improve the efficiency of GP. Our experimental results show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.

In this paper, we learn to discover composite operators and features that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROls) in images. Our approach is based on genetic programming (GP). The motivation for using GP is that there are a great many ways of combining these primitive operations and the human expert, limited by experience, knowledge and time. can only try a very small number of conventional ways of combination. Genetic programming, on the other hand, attempts many unconventional ways of combination that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. Our experimental results show that GP can find good composite operators to effectively extract the regions of interest in an image and the. learned composite operators can be applied to extract ROls in other similar images.

The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.

In this paper, we present an approach, to image segmentation in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. Image segmentation is guided by a genetic algorithm, which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce a candidate segmentation. The quality of each segmentation is evaluated within the genetic algorithm, by a comparison of two physics-based techniques for region growing and edge detection. Experimental results on real SAR imagery demonstrate that evolved segmentations are consistently better than segmentations derived from the Bayesian best single feature.

One of the fundamental weaknesses of computer vision systems used in practical applications was their inability to adapt the segmentation process as real-world changes occurred in the image. Presented is the first closed loop image segmentation system which incorporated a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem was formulated as an optimization problem and the genetic algorithm efficiently searched the hyperspace of segmentation parameter combinations to determine the parameter set which maximized the segmentation quality criteria. The goals of the adaptive image segmentation system were to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. Also presented are experimental results which demonstrated learning and the ability to adapt the segmentation performance in outdoor color imagery.

One of the fundamental weaknesses of computer vision systems used in practical applications was their inability to adapt the segmentation process as real-world changes occurred in the image. Presented is the first closed loop image segmentation system which incorporated a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem was formulated as an optimization problem and the genetic algorithm efficiently searched the hyperspace of segmentation parameter combinations to determine the parameter set which maximized the segmentation quality criteria. The goals of the adaptive image segmentation system were to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. Also presented are experimental results which demonstrated learning and the ability to adapt the segmentation performance in outdoor color imagery.